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This is one of the first studies demonstrating that the structural features of amorphous materials can be correlated with their physical properties. It is already feasible to synthesize amorphous materials with the same compositions but different thermal conductivities. However, the structural factors responsible for differences in thermal conductivity had yet to be identified due to a lack of appropriate analytical methods. It had been impossible to identify structural differences between amorphous germanium (Ge) materials with different thermal conductivities based only on high-resolution transmission electron microscope (TEM) observation. This research team analyzed amorphous Ge material TEM images using data science techniques—topological data analysis and principal component analysis—and identified structural differences between the materials. The team found that the atomic structures of thin film specimens deposited at lower temperatures tended to be dominated by smaller atomic rings (Ge25 in Figure 1) while specimens deposited at higher temperatures contained higher proportions of larger atomic rings (Ge300 in Figure 1). Figure 1. Distribution of atomic rings extracted from TEM images: Smaller (red dots) atomic rings are dominant in Ge25 while Ge300 contains a higher proportion of larger (blue dots) atomic rings. Larger atomic rings had been shown theoretically to be associated with higher thermal conductivity. This study found that Ge300 had higher thermal conductivity than Ge25—results consistent with the theoretical evidence. The data science techniques developed in this research project can be used to identify metastable phases in materials—a task impossible to achieve using conventional structural analysis techniques. These techniques are therefore expected to be useful in developing metastable phase-integrated thermal control materials. They may also be useful in identifying structural features associated with the mechanical, electrical and other properties of amorphous materials in addition to their thermal properties. This research was carried out by a research team consisting of Yibin Xu (Leader, Data-driven Inorganic Materials Group, Center for Basic Research on Materials, NIMS) and Kazuto Akagi (Associate Professor, Advanced Institute for Materials Research, Tohoku University). This work was conducted as part of a JST CREST Exploring Unknown Materials project (research supervisor: Professor Hiroshi Kitagawa, grant number: PMJCR21O2). It was also funded by JSPS Grant-in-Aid for Transformative Research (A) (project number: 22H05109) and Scientific Research (A) (project number: 20H00119). This research was published in the December 6, 2023 issue of the International Journal of Heat and Mass Transfer (vol. 221). Publication Details Title: Topological data analysis of TEM-based structural features affecting the thermal conductivity of amorphous Ge Authors: Yen-Ju Wu, Kazuto Akagi, Masahiro Goto and Yibin Xu Journal: International Journal of Heat and Mass Transfer DOI: 10.1016/j.ijheatmasstransfer.2023.125012 Contact Regarding this research Xu YibinGroup LeaderData-driven Inorganic Materials GroupData-driven Materials Research FieldCenter for Basic Research on MaterialsNational Institute for Materials Science E-mail: XU.Yibin&＃64;nims.go.jp Kazuto AkagiAssociate ProfessorMathematical Science GroupAdvanced Institute for Materials Research (AIMR), Tohoku University E-mail: kazuto.akagi.b5&＃64;tohoku.ac.jp Webstie: Profile of Dr. Akagi 研究内容に関すること Public Relations OfficeNational Institute for Materials Science Tel: +81-29-859-2026 Fax: +81-29-859-2017 E-mail: pressrelease&＃64;ml.nims.go.jp Strategic Public Relations OfficeAdvanced Institute for Materials Research (AIMR), Tohoku University E-mail: aimr-outreach&＃64;grp.tohoku.ac.jp Inquiries regarding JST research funding programs Public Relations DivisionDepartment of General AffairsJapan Science and Technology Agency E-mail: jstkoho&＃64;jst.go.jp Tweet Achievements Press Releases 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 Media & Award 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 AIMResearch About AIMResearch Research Highlights 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 In the Spotlight 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 Email Alert Sign up Publications Headlines 05/22/2024 Machine Learning Accelerates Discovery o... 05/16/2024 New Data-Driven Model Rapidly Predicts D... 05/15/2024 Researchers Unlock Vital Insights into M... 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